changeset 1:1b20ba32b4c5 draft

planemo upload for repository https://github.com/mvdbeek/dapars commit a02ced0b40d8946aa78a58321bd8f9771148391e-dirty

--- a/dapars.py	Tue Oct 27 10:14:33 2015 -0400
+++ b/dapars.py	Wed Oct 28 05:32:30 2015 -0400
@@ -82,7 +82,7 @@
         cmds = []
         for alignment_file in self.all_alignments:
             cmd = "sort -k1,1 -k2,2n tmp_bedfile.bed | "
-            cmd = cmd + "bedtools coverage -d -s -abam {alignment_file} -b stdin |" \
+            cmd = cmd + "~/bin/bedtools coverage -d -s -abam {alignment_file} -b stdin |" \
                         " cut -f 4,7,8 > coverage_file_{alignment_name}".format(
                 alignment_file = alignment_file, alignment_name= self.alignment_names[alignment_file] )
             cmds.append(cmd)
@@ -162,7 +162,8 @@
         return coverage_weights
 
     def get_result_tuple(self):
-        static_desc = ["chr", "start", "end", "strand", "gene", "breakpoint", "control_mean_percent", "treatment_mean_percent" ]
+        static_desc = ["chr", "start", "end", "strand", "gene", "breakpoint",
+                       "breakpoint_type", "control_mean_percent", "treatment_mean_percent" ]
         samples_desc = []
         for statistic in ["coverage_long", "coverage_short", "percent_long"]:
             for i, sample in enumerate(self.control_alignments):
@@ -181,13 +182,17 @@
                  "result_d":result_d}
         pool = Pool(self.n_cpus)
         tasks = [ (self.utr_coverages[utr], utr, utr_d, self.result_tuple._fields, self.coverage_weights, self.num_samples,
-                    len(self.control_alignments), len(self.treatment_alignments), result_d, self.search_start, self.coverage_threshold) for utr, utr_d in self.utr_dict.iteritems() ]
+                    len(self.control_alignments), len(self.treatment_alignments), self.search_start,
+                   self.coverage_threshold) for utr, utr_d in self.utr_dict.iteritems() ]
         processed_tasks = [ pool.apply_async(calculate_all_utr, t) for t in tasks]
         result = [res.get() for res in processed_tasks]
-        for d in result:
-            if isinstance(d, dict):
-                t = self.result_tuple(**d)
-                result_d[d["gene"]] = t
+        for res_control, res_treatment in result:
+            if isinstance(res_control, dict):
+                t = self.result_tuple(**res_control)
+                result_d[res_control["gene"]+"_bp_control"] = t
+            if isinstance(res_treatment, dict):
+                t = self.result_tuple(**res_treatment)
+                result_d[res_treatment["gene"]+"_bp_treatment"] = t
         return result_d
 
     def write_results(self):
@@ -199,99 +204,109 @@
 
     def write_bed(self):
         w = csv.writer(self.bed_output, delimiter='\t')
-        bed = [(result.chr, result.breakpoint, result.breakpoint+1, result.gene, 0, result.strand) for result in self.result_d.itervalues()]
+        bed = [(result.chr, result.breakpoint, result.breakpoint+1, result.gene+"_"+result.breakpoint_type, 0, result.strand) for result in self.result_d.itervalues()]
         w.writerows(bed)
 
+
 def calculate_all_utr(utr_coverage, utr, utr_d, result_tuple_fields, coverage_weights, num_samples, num_control,
-                      num_treatment, result_d, search_start, coverage_threshold):
-    res = dict(zip(result_tuple_fields, result_tuple_fields))
+                      num_treatment, search_start, coverage_threshold):
+    res_control = dict(zip(result_tuple_fields, result_tuple_fields))
+    res_treatment = res_control.copy()
     if utr_d["strand"] == "+":
         is_reverse = False
     else:
         is_reverse = True
-    mse, breakpoint, abundances = estimate_coverage_extended_utr(utr_coverage,
-                                                                 utr_d["new_start"],
-                                                                 utr_d["new_end"],
-                                                                 is_reverse,
-                                                                 coverage_weights,
-                                                                 search_start,
-                                                                 coverage_threshold)
-    if not str(mse) == "Na":
-        long_coverage_vector = abundances[0]
-        short_coverage_vector = abundances[1]
-        num_non_zero = sum((np.array(long_coverage_vector) + np.array(short_coverage_vector)) > 0)  # TODO: This introduces bias
-        if num_non_zero == num_samples:
-            percentage_long = []
-            for i in range(num_samples):
-                ratio = float(long_coverage_vector[i]) / (long_coverage_vector[i] + short_coverage_vector[i])  # long 3'UTR percentage
-                percentage_long.append(ratio)
-            for i in range(num_control):
-                res["control_{i}_coverage_long".format(i=i)] = float(long_coverage_vector[i])
-                res["control_{i}_coverage_short".format(i=i)] = float(short_coverage_vector[i])
-                res["control_{i}_percent_long".format(i=i)] = percentage_long[i]
-            for k in range(num_treatment):
-                i = k + num_control
-                res["treatment_{i}_coverage_long".format(i=k)] = float(long_coverage_vector[i])
-                res["treatment_{i}_coverage_short".format(i=k)] = float(short_coverage_vector[i])
-                res["treatment_{i}_percent_long".format(i=k)] = percentage_long[i]
-            control_mean_percent = np.mean(np.array(percentage_long[:num_control]))
-            treatment_mean_percent = np.mean(np.array(percentage_long[num_control:]))
-            res["chr"] = utr_d["chr"]
-            res["start"] = utr_d["start"]
-            res["end"] = utr_d["end"]
-            res["strand"] = utr_d["strand"]
-            if is_reverse:
-                breakpoint = utr_d["new_end"] - breakpoint
-            else:
-                breakpoint = utr_d["new_start"] + breakpoint
-            res["breakpoint"] = breakpoint
-            res["control_mean_percent"] = control_mean_percent
-            res["treatment_mean_percent"] = treatment_mean_percent
-            res["gene"] = utr
-            return res
+    control_breakpoint, \
+    control_abundance, \
+    treatment_breakpoint, \
+    treatment_abundance  = optimize_breakpoint(utr_coverage, utr_d["new_start"], utr_d["new_end"], coverage_weights,
+                                                 search_start, coverage_threshold, num_control)
+    if control_breakpoint:
+        breakpoint_to_result(res_control, utr, utr_d, control_breakpoint, "control_breakpoint", control_abundance, is_reverse, num_samples,
+                             num_control, num_treatment)
+    if treatment_breakpoint:
+        breakpoint_to_result(res_treatment, utr, utr_d, treatment_breakpoint, "treatment_breakpoint", treatment_abundance, is_reverse,
+                             num_samples, num_control, num_treatment)
+    return res_control, res_treatment
+
+
 
 
-def estimate_coverage_extended_utr(utr_coverage, UTR_start,
-                                   UTR_end, is_reverse, coverage_weigths, search_start, coverage_threshold):
+def breakpoint_to_result(res, utr, utr_d, breakpoint, breakpoint_type,
+                         abundances, is_reverse, num_samples, num_control, num_treatment):
+    """
+    Takes in a result dictionary res and fills the necessary fields
     """
-    We are searching for a breakpoint in coverage?!
-    utr_coverage is a list with items corresponding to numpy arrays of coverage for a sample.
+    long_coverage_vector = abundances[0]
+    short_coverage_vector = abundances[1]
+    num_non_zero = sum((np.array(long_coverage_vector) + np.array(short_coverage_vector)) > 0)  # TODO: This introduces bias
+    if num_non_zero == num_samples:
+        percentage_long = []
+        for i in range(num_samples):
+            ratio = float(long_coverage_vector[i]) / (long_coverage_vector[i] + short_coverage_vector[i])  # long 3'UTR percentage
+            percentage_long.append(ratio)
+        for i in range(num_control):
+            res["control_{i}_coverage_long".format(i=i)] = float(long_coverage_vector[i])
+            res["control_{i}_coverage_short".format(i=i)] = float(short_coverage_vector[i])
+            res["control_{i}_percent_long".format(i=i)] = percentage_long[i]
+        for k in range(num_treatment):
+            i = k + num_control
+            res["treatment_{i}_coverage_long".format(i=k)] = float(long_coverage_vector[i])
+            res["treatment_{i}_coverage_short".format(i=k)] = float(short_coverage_vector[i])
+            res["treatment_{i}_percent_long".format(i=k)] = percentage_long[i]
+        control_mean_percent = np.mean(np.array(percentage_long[:num_control]))
+        treatment_mean_percent = np.mean(np.array(percentage_long[num_control:]))
+        res["chr"] = utr_d["chr"]
+        res["start"] = utr_d["start"]
+        res["end"] = utr_d["end"]
+        res["strand"] = utr_d["strand"]
+        if is_reverse:
+            breakpoint = utr_d["new_end"] - breakpoint
+        else:
+            breakpoint = utr_d["new_start"] + breakpoint
+        res["breakpoint"] = breakpoint
+        res["breakpoint_type"] = breakpoint_type
+        res["control_mean_percent"] = control_mean_percent
+        res["treatment_mean_percent"] = treatment_mean_percent
+        res["gene"] = utr
+
+
+def optimize_breakpoint(utr_coverage, UTR_start, UTR_end, coverage_weigths, search_start, coverage_threshold, num_control):
+    """
+    We are searching for a point within the UTR that minimizes the mean squared error, if the coverage vector was divided
+    at that point. utr_coverage is a list with items corresponding to numpy arrays of coverage for a sample.
     """
     search_point_end = int(abs((UTR_end - UTR_start)) * 0.1)  # TODO: This is 10% of total UTR end. Why?
     num_samples = len(utr_coverage)
-    ##read coverage filtering
-    normalized_utr_coverage = [coverage/ coverage_weigths[i] for i, coverage in enumerate( utr_coverage.values() )]
+    normalized_utr_coverage = np.array([coverage/ coverage_weigths[i] for i, coverage in enumerate( utr_coverage.values() )])
     start_coverage = [np.mean(coverage[0:99]) for coverage in utr_coverage.values()]  # filters threshold on mean coverage over first 100 nt
     is_above_threshold = sum(np.array(start_coverage) >= coverage_threshold) >= num_samples  # This filters on the raw threshold. Why?
     is_above_length = UTR_end - UTR_start >= 150
     if (is_above_threshold) and (is_above_length):
-        if not is_reverse:
-            search_region = range(UTR_start + search_start, UTR_end - search_point_end + 1)
-        else:
-            search_region = range(UTR_end - search_start, UTR_start + search_point_end - 1, -1)
         search_end = UTR_end - UTR_start - search_point_end
-        normalized_utr_coverage = np.array(normalized_utr_coverage)
         breakpoints = range(search_start, search_end + 1)
-        mse_list = [ estimate_mse(normalized_utr_coverage,bp, num_samples) for bp in breakpoints ]
+        mse_list = [ estimate_mse(normalized_utr_coverage, bp, num_samples, num_control) for bp in breakpoints ]
         if len(mse_list) > 0:
-            min_ele_index = mse_list.index(min(mse_list))
-            breakpoint = breakpoints[min_ele_index]
-            UTR_abundances = estimate_abundance(normalized_utr_coverage, breakpoint, num_samples)
-            select_mean_squared_error = mse_list[min_ele_index]
-            selected_break_point = breakpoint
-        else:
-            select_mean_squared_error = 'Na'
-            UTR_abundances = 'Na'
-            selected_break_point = 'Na'
-    else:
-        select_mean_squared_error = 'Na'
-        UTR_abundances = 'Na'
-        selected_break_point = 'Na'
-
-    return select_mean_squared_error, selected_break_point, UTR_abundances
+            return mse_to_breakpoint(mse_list, normalized_utr_coverage, breakpoints, num_samples)
+    return False, False, False, False
 
 
-def estimate_mse(cov, bp, num_samples):
+def mse_to_breakpoint(mse_list, normalized_utr_coverage, breakpoints, num_samples):
+    """
+    Take in mse_list with control and treatment mse and return breakpoint and utr abundance
+    """
+    mse_control = [mse[0] for mse in mse_list]
+    mse_treatment = [mse[1] for mse in mse_list]
+    control_index = mse_control.index(min(mse_control))
+    treatment_index = mse_treatment.index(min(mse_treatment))
+    control_breakpoint = breakpoints[control_index]
+    treatment_breakpoint = breakpoints[treatment_index]
+    control_abundance = estimate_abundance(normalized_utr_coverage, control_breakpoint, num_samples)
+    treatment_abundance = estimate_abundance(normalized_utr_coverage, treatment_breakpoint, num_samples)
+    return control_breakpoint, control_abundance, treatment_breakpoint, treatment_abundance
+
+
+def estimate_mse(cov, bp, num_samples, num_control):
     """
     get abundance of long utr vs short utr with breakpoint specifying the position of long and short utr.
     """
@@ -303,8 +318,10 @@
         mean_short_utr = np.mean(short_utr_vector, 1)
         square_mean_centered_short_utr_vector = (short_utr_vector[:num_samples] - mean_short_utr[:, np.newaxis] )**2
         square_mean_centered_long_utr_vector = (long_utr_vector[:num_samples] - mean_long_utr[:, np.newaxis])**2
-        mse = np.mean(np.append(square_mean_centered_short_utr_vector[:num_samples], square_mean_centered_long_utr_vector[:num_samples]))
-        return mse
+        mse_control = np.mean(np.append(square_mean_centered_long_utr_vector[:num_control], square_mean_centered_short_utr_vector[:num_control]))
+        mse_treatment = np.mean(np.append(square_mean_centered_long_utr_vector[num_control:], square_mean_centered_short_utr_vector[num_control:]))
+        return mse_control, mse_treatment
+
 
 def estimate_abundance(cov, bp, num_samples):
     with warnings.catch_warnings():